Confidence-aware self-supervised learning for dense monocular depth estimation in dynamic laparoscopic scene.

ABSTRACT: This paper tackles the challenge of accurate depth estimation from monocular laparoscopic images in dynamic surgical environments. The lack of reliable ground truth due to inconsistencies within these images makes this a complex task. Further complicating the learning process is the presence of noise elements like bleeding and smoke. We propose a model learning framework that uses a generic laparoscopic surgery video dataset for training, aimed at achieving precise monocular depth estimation in dynamic surgical settings. The architecture employs binocular disparity confidence information as a self-supervisory signal, along with the disparity information from a stereo laparoscope. Our method ensures robust learning amidst outliers, influenced by tissue deformation, smoke, and surgical instr

SUBMITTER: Hirohata Y 

PROVIDER: S-EPMC10505201 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature